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Photovoltaic (PV) power generation systems are highly subject to weather and site conditions, thus, the construction of PV power plant projects must consider these uncertainties. This study analyzes the monthly electricity generation of 249 utility-scale PV power plants in Japan to evaluate their electricity generation efficiency. Applying the generic data envelopment analysis, benchmark values were identified for power generation from PV power plants. Furthermore, we implemented a Monte Carlo experiment to evaluate the impact of variability in solar irradiance and temperature on power generation efficiency. For our analysis, we considered three inputs—solar irradiance, temperature, and installed capacity—and electricity generation as the output. The results showed that inter-regional gap in the efficiency score between the west and north regions is 0.03, and this can be covered by a 0.1 increase in the DC/AC ratio. Additionally, variability in weather conditions affect both the efficiency of a power plant and production possibility frontier, in turn causing the benchmark values for a generic decision-making unit to vary. Increasing the generation capacity of power plants and operating them more efficiently is essential to expanding the use of renewable energy resources. • Plants are subject to the uncertainties of weather and site conditions. • Efficiency score is higher during spring and summer. • Northern and western regions have an inter-regional efficiency gap. • Scale efficiency tends to increase with increasing installed capacity. • Monthly power generation benchmarks are identified. [ABSTRACT FROM AUTHOR]

The existing studies that measure the performance of university technology transfer have primarily treated technology transfer as an independent process, neglecting the competitive dynamics within universities and between universities. To bridge this gap, we extend the existing data envelopment analysis (DEA) game models by introducing an innovative dual-game cross-efficiency model. This model comprehensively accounts for the competitive interactions between university research and technology transfer, as well as the competition among universities, measuring technology transfer efficiency within a more integrated competition framework. The efficiency derived from our model integrates both self-evaluation of sub-stages within DMUs and peer-evaluation from other DMUs, making it more acceptable to both internal and external evaluated units. Employing this model, we assessed the efficiencies of technology transfer and research of universities across 30 Chinese provinces. Our analysis reveals that the average efficiency of technology transfer among Chinese universities is 0.3321, which is significantly lower than the average research efficiency of 0.6536. Additionally, the study uncovers significant regional disparities in the efficiency of university technology transfer, indicating a need for tailored strategies to enhance efficiencies in various universities. Finally, comparison with traditional models demonstrates the superiority of our model in both acceptability and comprehensiveness. • A novel dual-game cross-efficiency model was introduced. • An improved iteration algorithm for the final set of cross-efficiencies was proposed. • We incorporated an integrated competition framework for universities. • We focused on the performance evaluation of HEI's technology transfer. [ABSTRACT FROM AUTHOR]

This study addresses the global concern about undesirable outputs in the Forest Sector. We propose two innovative models, namely a directional weak disposable DEA model and an extended stochastic DEA model, to measure environmental efficiency. These models make a significant contribution to the field by specifically assessing the uncertain environmental efficiency of the forest sector. We validated our proposed models by conducting an empirical application using the United Nations Economic Commission for Europe (UNECE) forest sector dataset. The study examines important outputs such as above ground biomass stock, export unit prices of industrial roundwood, wood removals (desirable outputs), and CO2 emissions from wildfires (undesirable output). The results demonstrate that our novel stochastic weak disposability DEA model outperforms traditional approaches when the second scenario is applied. Specifically, the average technical efficiency (TE) score decreases to 0.92, and the number of efficient units reduces to 27, representing an approximate improvement of 55 %. Furthermore, the reduction rate of CO2 emissions is 4.09 % lower than the benchmark. Hence, our extended novel stochastic weak disposability DEA approach enhances the assessment of efficiency and inefficiency in decision-making units, contributing to the mitigation of risk and uncertainty. It also improves overall environmental performance in forest management. • This study addresses the global concern about undesirable outputs in the Forest Sector. • We developed a stochastic data envelopment analysis extension based on weak disposability assumption. • Empirical application on United Nations Economic Commission for Europe Forests is used. • We showed that our stochastic data envelopment analysis model performs better than previous approaches. • Proposed model contributes to enhancing total environmental performance in forest management. [ABSTRACT FROM AUTHOR]

Performance assessment of Intercity Road Freight Transport (IRFT) involve a complex set of considerations to ensure the efficient transportation of goods between their origins and destinations. Notably, Data Envelopment Analysis (DEA) models are often the preferred approach for conducting assessment studies that involve multiple inputs and outputs. Even though the conventional Cross-Efficiency (CE) technique addresses the limitations of DEA in producing comparable results, it still fails to consider the evaluation of internal system operations. The use of Network CE leads to more realistic results in assessing problems. It addresses not only the limitations of the DEA model in producing comparable results but also considers the performance of internal operations and system efficiency. Incorporating CE into a Parallel Network System (PNS), which is known as CE-PNS, has also a significant drawback as it neglects both the optimistic and pessimistic viewpoints based on the efficient and anti-efficient frontiers simultaneously, and may therefore fail to provide a comprehensive analysis. The primary objective of this study is to propose a new double-frontier network CE that takes into account two perspectives simultaneously. Indeed, this study is the first to incorporate the concepts of CE and double-frontier DEA into a PNS to provide an effective tool for assessing Iranian IRFT. For this purpose, the Cross-Inefficiency (CIE) technique is first incorporated into the PNS, called CIE-PNS, which explores the relationship between system inefficiency and internal divisions. The CE-PNS and CIE-PNS may yield different results. Therefore, Double-Frontier CE for a PNS (DFCE-PNS) is derived by combining the efficiency results obtained from CE-PNS and CIE-PNS. This study also examines different inputs and outputs to assess the performance of Iranian IRFT. The results demonstrate that DFCE-PNS yields more comprehensive and reliable results. • Incorporation of optimistic and pessimistic CE into a PNS. • Development of CIE-PNS to incorporate the concept of CE into a PNS. • Development of DFCE-PNS as a comprehensive evaluation technique. • Definition of useful KPIs to implement the DFCE-PNS in a case study. [ABSTRACT FROM AUTHOR]

Due to the scarcity of fossil fuels in the future, the optimal use of these products can not only increase the efficiency of power plants, but it can also be effective in reducing the production of pollutants. To deal with these situations, optimal resource allocation and reallocation was studied using the data envelopment analysis (DEA) models. The current study adopted a resource allocation model in DEA framework when undesired outputs are produced in production process. This alternative resource allocation model is, however, sensitive to uncertainty of the data. In this contribution, we, therefore, introduce a stochastic resource allocation model when there are random data and undesirable products. An applied illustrative study to the power industry consisting 21 electricity production & distribution companies for eight years (2011–2019) is performed to compare the resource reallocations and their efficiencies. The important findings are: First, if we decide to deactivate two companies, the fuel consumption, employees and net electricity generation must be reduced. These reductions will lead to a reduction in pollutants. Second, the low price of electricity in Iran leads to excessive consumption of this product, which in turn leads to the inefficiency of many companies. In order to improve the performances of the companies, the amount of sold-out electricity must significantly be increased. • This paper adopted a resource allocation model in DEA with undesired outputs. • Our resource allocation model is sensitive to uncertainty of the data. • A stochastic resource allocation model is given when we have random data and undesirable products. • An application on 21 electricity production & distribution companies for eight years is given. [ABSTRACT FROM AUTHOR]

This paper delves into the complexities of assessing educational performance and efficiency in the context of education governance. We propose a novel perspective on how governing features of educational systems influence their efficiency, focusing on the key features of public bodies responsible for local education management in Peru, known as UGELs. We characterize the educational production function using three inputs: planning conditions, human resources, and accountability mechanisms; and two outputs: student achievement and educational progress. Our efficiency estimation utilizes Robust Free Disposal Hull, Robust Data Envelopment Analysis, and Stochastic Non-parametric Envelopment approaches, leveraging unique government data from 2014. Additionally, we explore potential factors like infrastructure and internet access that could improve UGELs' efficiency through conditional DEA analysis. Our findings highlight the need for nuanced methodologies in evaluating educational performance and reveal a significant gap in existing literature. This paper addresses this gap by offering a comprehensive evaluation framework, emphasizing the importance of thorough assessments for gauging educational institution efficiency. As policymakers increasingly rely on evidence-based decision-making, our research provides valuable insights that can inform and shape education policy decisions, making it a significant contribution to the field of education policy and governance. • Aim : Assessing educational performance and efficiency from governance perspective. • Focus : Local bodies (UGELs) in Peru. • Analysis : Inputs (planning, resources, accountability) & outputs (achievement, progress). • Advanced methods : FDH, DEA, StoNED & conditional DEA. • Impact : Comprehensive evaluation framework for educational governance assessment. [ABSTRACT FROM AUTHOR]

This article proposes nonparametric and parametric methods for conducting meaningful evaluations of academic programs. In particular, it proposes the use of data envelopment analysis (DEA) along with regression analysis for the evaluation of Spanish public universities' undergraduate curricula. Unlike the radial DEA models, which assume an equiproportional expansion of all outputs to achieve efficiency, the current study also relies on the use of non-radial DEA models for assessing the efficiency of Spanish public universities. A non-radial DEA measure (the so-called Russell measure) allows for unlike proportional augmentations in each positive output. In this study, non-radial DEA measures allow us to identify different levels of inefficiency for each output considered (social sciences and non-social sciences degrees). Specifically, the results show that the inefficiency in the production of scientific and technical degrees is greater than that in the production of humanities and social sciences degrees, although there are differences among institutions. Bachelor's degree production time is also estimated, and a duration analysis explains the time to degree. A worrying result is the "excessive" time it takes for a university to produce four-year undergraduate degree programs. The mean graduation time is 5.7 years. The Cox proportional hazards regression shows that a shorter time to graduation is associated with higher teaching quality as well as a higher faculty-to-student ratio. Parametric survival analysis using a lognormal distribution with gamma frailty also verifies these latter results. • Data envelopment analysis (DEA) is proposed for the evaluation of Spanish public universities' undergraduate curricula. • Radial and non-radial DEA models are used to assess the teaching performance of universities. • Non-radial models allow for identifying inefficiencies in the production of social sciences and non-social sciences degrees. • Bachelor's degree production times are also estimated, and a duration analysis explains the time-to-degree. [ABSTRACT FROM AUTHOR]

The Cross-Efficiency (CE) method, based on Data Envelopment Analysis (DEA), is a commonly used approach to measure the efficiency of Decision-Making Units (DMUs). Various secondary goal models can yield different efficiency results. The recently developed Game CE (GCE) method aims to address this issue and achieve convergence efficiencies. However, the conventional GCE was developed under the unrealistic assumption that Decision Makers (DMs) are perfectly rational, as it relies on the arithmetic mean aggregation method. Additionally, the GCE evaluates DMUs based on efficient reference points, disregarding the pessimistic perspective. The study introduces a new aggregation method called Regret-Consensus Aggregation (RCA) that captures the subjective preferences of DMs. GCE-RCA scores are obtained by incorporating RCA into the GCE. A new method called Game Cross In-Efficiency (GCIE) with RCA (GCIE-RCA) has been developed to incorporate anti-efficient reference points into the evaluation process. A Double-Frontier GCE based on the RCA (DFGCE-RCA) is developed from two perspectives. The DFGCE-RCA is implemented to evaluate the performance of the Iranian Inter-City Road Passenger Transportation (IC-RPT) system based on six key performance indicators. DFGCE-RCA has several advantages in addition to those of GCE. Firstly, considering two perspectives provides more comprehensive results. Additionally, it can capture the subjective perspectives of DMs. Lastly; it enables the aggregation process to reach a consensus. • Game Cross Inefficiency (GCIE) method was originally developed. • The Regret-Consensus-Aggregation (RCA) method is a cross-evaluation aggregation method that has been developed. • RCA is incorporated into GCE and GCIE methods to capture the subjective preferences of decision-makers. • The double-frontier GCE with RCA incorporates both optimistic and pessimistic perspectives into the evaluation process. • The evaluation of Iranian Inter-City Road Passenger Transportation (IC-RPT) has been conducted using the proposed technique. [ABSTRACT FROM AUTHOR]

While resilience analysis can provide important insights into risk management strategies, research gaps exist in terms of logistics efficiency, regional economic environment, and spatial clustering patterns. Therefore, our study capitalizes on the dramatic variations in logistics activities among regions during COVID-19 to examine regional logistics resilience in the disaster and explore potential spatial clustering patterns. We measure regional logistics resilience through changes in performance outcomes, specifically efficiency fluctuation. To analyze spatial clustering patterns, (regional heterogeneity) presented by logistics efficiency, we propose a new multi-stage approach that integrates the data envelopment analysis with the K-medoids algorithm and analyzes data from the Chinese logistic industry. The results reveal that efficiency fluctuations exhibit distinct spatial distribution characteristics, with more pronounced negative fluctuations in areas of important logistics activities nodes and islands, and more widespread negative trends in coastal areas. Moreover, regions with high freight volumes and logistics specialization demonstrate a sustained high level and quality of logistics efficiency. Furthermore, consumption capacity and economic development appear to positively influence fluctuations in logistics efficiency. The findings hold implications for enhancing regional logistics resilience in the disaster and contribute valuable insights to regional logistics risk management. • We leverage the dramatic variations in logistics activities during COVID-19. • We integrate the data envelopment analysis model with the K-medoids algorithm. • We explore the resilience of regional logistics through efficiency fluctuations. • We investigate the spatial clustering patterns in logistics efficiency. • Implications of the multi-model method and regional logistics resilience. [ABSTRACT FROM AUTHOR]

Due to the challenges of implementing environmental responsibility, sustainable development is often criticized for its promise to "square the circle". This paper incorporates sociological theories into operational research to enhance our understanding of the issue. It proposes a framework for evaluating eco-efficiency that captures the decision-making process from a macro perspective rather than focusing on a single technique or employing a multimethodology. By utilizing 272 Chinese cities as case studies, the evaluation process is effectively implemented. From three perspectives - region, city size, and policy - we draw some interesting conclusions. Finally, a vision for future research is presented in the concluding section. • This study incorporates sociological theories into operational research to enhance the understanding of eco-efficiency evaluations. • By utilizing 272 Chinese cities as case studies, we implement a comprehensive and effective evaluation process. • We propose a visionary framework and four potential directions for further exploration. [ABSTRACT FROM AUTHOR]

We introduce benchmarking analysis based on state-of-the-art machine learning techniques applied to the measurement of efficiency to assess the performance of Higher Education Institutions (HEIs). We rely on Efficiency Analysis Trees (EAT) and its Convexified frontier counterpart (CEAT) to assess the efficiency of 144 private HEIs in Colombia and compare the results with those achieved with classical Data Envelopment Analysis (DEA). Both EAT and CEAT show a higher discriminatory power than DEA when determining efficiency scores. Our results identify the different splits of the production frontier, corresponding to each node of the efficiency tree, which groups HEIs according to specific management models. By identifying relevant peers for inefficient observations at the node level, we show which strategic guidelines can be adopted to improve the performance of each HEI. This process encourages mutual learning and suggests potential changes within each node leading to efficiency improvements. • Machine learning methods represent an alternative to traditional envelopment techniques. • We introduce how to benchmark units through EAT and CEAT. • We apply these methods on 144 private HEIs in Colombia. • We provide strategic guidelines aimed at improving the performance of HEIs. • We identify relevant benchmarks and the corresponding output adjustments leading to efficiency enhancement. [ABSTRACT FROM AUTHOR]

This paper constructs a two-stage undesirable and desirable congestion model, which distinguishes purely technical inefficiency from congestion, to study the energy congestion of 30 transportation sectors in China from 2011 to 2020. The Heckman model is used to analyze the influence mechanism of various factors on the undesirable congestion of energy, which avoids sample selection bias. The results show that: (1) The undesirable congestion of energy mainly exists in the transport sector of the western region, while the desirable congestion of energy only exists in the transport sectors of the eastern and central regions. (2) Purely technical inefficiency in the transportation sector does not usually occur in isolation. (3) Population density and the proportion of clean freight turnover have a negative impact on the undesirable congestion of energy. • A two-stage undesirable and desirable congestion model is designed innovatively. • Energy efficiency in transport sector is assessed from the perspective of congestion. • The factors affecting the undesirable congestion in transport sectors are analyzed. • Rational allocation of energy in transport sector is realized based on congestion. [ABSTRACT FROM AUTHOR]

This research aims at investigating the relationship between efficiency, sustainability, and environmental risk in the European agricultural sector. On the one hand, we assess whether efficiency and sustainability are complementary or competing targets; on the other, we explore to what extent environmental risk exposure can represent a motivation or a threat to promoting sustainability in the sector. To do so, we construct three composite indicators, based on frontier techniques. In particular, we implement classical Data Envelopment Analysis (DEA) to estimate agricultural efficiency, a DEA model for undesirable output for the sustainable efficiency, and a Benefit of the Doubt model to assess environmental risk. The analysis highlights three main results: first, that efficiency and sustainability are not competing aims, at least for the analysed territories; second, countries that are exposed to environmental risk are less agricultural efficient; third, more sustainable countries are likely to be the most exposed to environmental risk, indicating that direct exposure to environmental risks might be a strong motivation engine to encourage sustainability. The study adds to previous literature debate by providing evidence about the agricultural sector's vulnerability and of the difficulties of combining efficiency, sustainability, and environmental goals. • Frontier approaches for agricultural, sustainable and environmental efficiency. • Exploring interactions between efficiency, sustainability, and environmental risk. • Efficiency and sustainability are not competing aims. • Countries that are exposed to environmental risk are less agricultural efficient. • More sustainable countries are likely to be the most exposed to environmental risk. [ABSTRACT FROM AUTHOR]

How to assess the performance of funds to support the rural revitalization is closely concerned by both academics and industries. In this paper, taking agricultural loans and financial allocations as inputs, we open the dynamic internal structure of the funding to support the rural revitalization system (FSRRS), including the agricultural support funds system (ASFS) and the rural revitalization system (RRS). With the consideration of regional heterogeneity, we propose the dynamic network data envelopment analysis (DEA) models based on convex and nonconvex metafrontiers to measure the metatechnology-specific technical efficiency (MTE), group-specific technical efficiency (GTE) and technology gap ratio (TGR) of the overall system and subsystems. The proposed models are empirically applied to assess the efficiency of funding to support rural revitalization and regional heterogeneity of technologies for 31 provinces in China during 2017–2020. The results show that: (i) compared to the nonconvex metafrontier model, its convex counterpart underestimates the efficiency of each decision making unit (DMU), resulting in the unfairness of evaluation, in particular for the central and western regions; (ii) there is a difference in the efficiency of the overall system and the subsystems, where the RRS performance is superior to the ASFS performance, implying that the inefficiency of FSRRS is mainly attributed to the low utilization of agricultural support funds; (iii) the period efficiency of the overall system and subsystems show an increasing trend over time, and the magnitudes of increases are different across provinces (regions); (iv) the TGRs of various provinces (regions) exhibit a significant heterogeneity. • We open the dynamic internal structure of the funding to support rural revitalization system (FSRRS). • We propose nonconvex metafrontiers based models to measure the performance of FSRRS. • We discuss the efficiency of FSRRS and regional heterogeneity of technologies. • We provide theoretical and empirical evidence for the model selection of FSRRS evaluation. [ABSTRACT FROM AUTHOR]

While the imperative of gauging school education development across regions is widely acknowledged, a scarcity of methodologically robust measures to quantify it persists. This paper introduces an innovative non-parametric framework for constructing an all-inclusive school education development index (SEDI) across regional entities. The proposed framework, termed "meta-RDM-BoD", seamlessly integrates three distinct yet interconnected non-parametric efficiency modeling approaches: the range directional measure (RDM) proposed by Portela et al. (2004) [5], the meta-frontier analysis developed by O'Donnell et al. (2008) [6], and the benefit-of-the-doubt (BoD) technique put forth by Melyn and Moesen (1991) [7]. Notably, the proposed framework adeptly handles both desirable and undesirable indicators, accommodates indicators with negative and zero values without compromising the properties of translation and unit invariance, and effectively accounts for underlying heterogeneity across regional entities. To illustrate the efficacy of the SEDI, we provide a compelling example using data on 36 school education indicators for Indian states and union territories in 2021–2022. These indicators cover five crucial dimensions of school education: access to school, school infrastructure and facilities, teacher quality, school outcomes, and equity in education. The results reveal spatial gaps in school education development, offering valuable insights for benchmarking, ranking, and classifying regional entities. [Display omitted] • Development of "meta-frontier Range Directional Measure BoD" framework for a school education development index. • Framework handles the zero and negative data values and accounts for heterogeneity across regions. • Helpful for policymakers to unveil spatial gaps and benchmark, rank, and classify regional entities. • Illustration is presented using 36 school education indicators for Indian states. [ABSTRACT FROM AUTHOR]

We introduce an algorithmic framework for investigating the robustness of efficiency analysis results in the presence of imprecise information about data and preferences. We employ an additive value efficiency model accepting ordinal and interval information about the performances of Decision Making Units and imprecision in the specification of input and output weights and the shapes of marginal value functions. We verify the stability of efficiency measures using a combination of mathematical programming and Monte Carlo simulations. The results capture various certainty levels, emphasizing the necessary, possible, extreme, and expected outcomes and the distribution of outcomes in the space of feasible weights, performances, and marginal functions. The practical usefulness of the proposed framework is demonstrated in a real-world problem concerning the functioning of Special Economic Zones in Poland. We discuss results that increase the discrimination power, indicate overall good performances, and provide hints on the required improvements. • We measure the efficiency using a suitably adapted additive value model. • We admit imprecision in the specification of performances and efficiency models. • We analyze the robustness of efficiency in terms of four types of results. • We cross-fertilize Data Envelopment Analysis and Multiple Criteria Decision Aiding. • We apply the framework to the efficiency analysis of Polish Special Economic Zones. [ABSTRACT FROM AUTHOR]

This work delves into the crucial role of service quality in the water supply and sanitation sector. Despite extensive research and implementation of quality management practices in this sector, a universally accepted definition of quality is still lacking, resulting in various service quality assessment procedures that are difficult to compare. To address this issue, the World Bank launched the 'Utility of the Future' (UoF) programme, aiming to guide water service providers in their efforts to become future-focused utilities that offer reliable, safe, inclusive, transparent, and responsive services through best-fit practices. Building upon the framework provided by the UoF programme, this study proposes the Water Utility Service Quality Index (WUSQI) - a composite indicator that reflects the quality of service provided by water supply and sanitation utilities from a customer perspective. Based on Data Envelopment Analysis, the Benefit-of-the-Doubt approach is employed to assign weights for aggregating the indicators representing the diverse performance dimensions. The study operationalises the WUSQI to assess the quality of Portuguese wholesale water and wastewater companies using data collected by the national regulator of water and waste services. A Multiple Criteria Decision Analysis technique, the Deck of Cards method, is used to specify an indicator of transparency from the information made available by the regulated utilities. The results show the effectiveness of this tool for evaluating and measuring service quality at the company level. Additionally, the findings highlight areas for improvement in the utilities' performance. By enabling companies and regulators to identify areas for improvement, the WUSQI can support the delivery of high-quality services to customers. • Assessing service quality is vital for actionable improvement planning. • Composite indicators can measure multidimensional information effectively. • 'Utility of the Future' framework can assess water service quality globally. • Customer-centred approach can define water utility service level for satisfaction. [ABSTRACT FROM AUTHOR]

Healthcare delivery in public hospitals is operated and managed individually, while performance monitoring, budget and human resource allocation are administered through the Ministry of Health state offices, reflecting the dependency between the two decision units. The geographical distribution of hospitals varies within the states of Malaysia, which requires an efficiency measurement that assesses the relationship between both the state- and hospital-level efficiencies simultaneously. The main objective of this study was to assess the performance of hospital of each decision unit at the state level. The study uses an extended multi-period parallel data envelopment analysis model to assess the multi-period performance of hospital systems, with the characteristics of heterogeneous structure, operating under the Ministry of Health state-level offices. The results showed that the efficiency score for all states ranged between 0.45 and 0.56, while the period-state weights for all states ranged between 0.30 and 0.33. The study utilizes our proposed method of aggregating the period state and hospital data for an overall state efficiency score, providing a more objective measurement compared to the arithmetic mean of efficiency scores. Such results could also be used to justify the mobilization of resources from a performing state to a less performing state. Furthermore, information on endogenous weight could assist policymakers in prioritizing strategic plans to enhance the overall performance of health services at both the hospital and state levels. • The study aims to evaluate the performance of each decision unit's hospital at the state level. • A multi-period parallel data envelopment analysis model is used in the study. • Applying objective measurement in aggregating state and hospital efficiency. • Assisting policymakers in prioritizing strategic plans to improve the overall performance of health services. [ABSTRACT FROM AUTHOR]

Investigating whether industry transfers to different cities and regions contribute to innovative efficiency improvement is a critical issue in addressing the development of urban innovation. Using the implementation of national industrial relocation demonstration zones (NIRDZ) in China as a quasi-natural experiment, this study empirically assesses the impact of industrial relocation upon urban innovation efficiency. This study first measures the urban innovation efficiency of 194 prefectural cities during 2005–2019 using a two-stage data envelopment analysis model. Then, a multiperiod difference-in-differences model is used to evaluate the innovation effects of national-level demonstration zones for undertaking industrial transfers. The results are as follows. First, industrial relocation does contribute to urban innovation efficiency. This conclusion is supported by a series of robustness tests confirming that the NIRDZ pilot policy indeed contributes to this efficiency, including a placebo test, propensity score matching, shortened time window, winsorized outliers, and lag treatment. Secondly, a regional heterogeneity analysis shows that the pilot policy significantly promotes innovation efficiency of cities located in the central region, but there is an insignificant effect in western region cities. The analysis of city size heterogeneity shows that the pilot policy promotes innovation efficiency more significantly in small cities. In contrast, the promotion effect on big cities is not significant. Third, when considering the potential impact mechanisms, the NIRDZ policy positively affects urban innovation efficiency by enhancing the informatization level and improving economic development. Based on the analysis above, this study recommends establishing an effective mechanism for industrial transfer, realizing the transfer between different regions and cities according to local conditions, and deepening the NIRDZ policy implementation. Policymakers should also increase investment in the construction of an informatization platform, plan resource usage rationally, promote regional integration development, change the philosophy of education, and recognize how the NIRDZ policy influences urban innovation through economic development and informatization. • National industrial relocation policy improved urban innovation efficiency. • National industrial relocation policy works by enhancing informatization level. • National industrial relocation policy works by improving economic development. • The policy effect is heterogeneous in terms of city size and different regions. • A two-stage DEA model and a difference-in-differences model are used in this study. [ABSTRACT FROM AUTHOR]

Evaluating the resource utilization efficiency of a country's universities is a prerequisite for implementing a balanced development strategy. Universities often share some resources such as the teachers and fixed assets used for teaching and research. Data envelopment analysis (DEA), as a non-parametric method for performance evaluation of decision-making units (DMUs), has been used for measuring the operating efficiency of universities with shared inputs based on radial measures under multiplier DEA models. However, few existing studies consider non-radial measures and different perspectives of external and internal evaluations. From both external and internal perspectives, we propose aggregated two-stage DEA models to measure the efficiency scores of 52 Chinese universities in 2014. The main results indicate that: (1) both internal and external evaluations yield relatively high average operating efficiency, with around 53 % of universities being efficient. The allocation ratios of shared inputs for the same university differ according to the internal and external evaluations. (2) Heterogeneity analysis reveals that 985 universities are less efficient than non-985 universities, and comprehensive universities operate more efficiently than science and engineering universities. (3) There are efficiency differences between 985 and non-985 universities in the teaching stage. Science and engineering as well as comprehensive universities also differ in the teaching efficiency. • Analyze the non-radial efficiency scores of 52 Chinese universities in 2014. • Propose aggregated two-stage DEA models with shared inputs via external and internal evaluations. • Both internal and external evaluation yield relatively high average operating efficiency. • Reveal the efficiency heterogeneity of different universities from two evaluation perspectives. [ABSTRACT FROM AUTHOR]

The national economy heavily relies on the banking sector, and researchers have been focused on discovering the most effective method of enhancing bank's overall efficiency. Data Envelopment Analysis (DEA) is a commonly utilized tool for measuring banking efficiency. We proposed an improved slacks-based measure model (SBM) with undesirable outputs using a by-production framework. In such case, the bank's operating process is considered as a parallel system. Inputs are categorized into interest costs and operating costs, which are then evaluated in two separate parallel stages. In the first stage, outputs are categorized into interest income and non-interest income, which are shared and considered as final outputs. In the second stage, non-performing loans are deemed as undesirable outputs. This proposed approach splits bank efficiency into two parallel stages, enabling us to identify the source of overall inefficiency, and effectively capture the efficiency differences between the two types of costs. To demonstrate the feasibility of the proposed approach, we have successfully applied it to 36 commercial banks in China from 2016 to 2021. The empirical results of the research prove that the changes of the banks' overall efficiencies were mainly due to the changes of stage 2. And this approach provides a rich source of information to assist in decision making. • To analyze the efficiency of commercial banks by taking into account their internal structure. • To develop an enhanced slacks-based measure model (SBM) that incorporates undesirable outputs. • To assess the operating process of banks using a parallel structure approach. [ABSTRACT FROM AUTHOR]

Analyzing engineering colleges in India with Data Envelopment Analysis (DEA) is the focus of this study. Engineering colleges pioneer in teaching and research, creating two sub-processes within engineering education. Thus, our study considers each engineering college to comprise two parallel non-homogenous sub-processes, such as teaching and researching. Unlike the conventional DEA models, where the system is treated as a black box, this work attempts to independently assess the impact of each parallel sub-process so that it is possible to identify specific changes in the respective processes to improve the overall efficiency. In particular, our study analyzed the efficiency of engineering colleges based on the National Institute Ranking Framework (NIRF) data. Further, the comparison of the efficiency scores from teaching and research indicates that, on average, the institutes are more efficient in teaching than researching. The current study helps policymakers set priorities and course corrections for performance improvement programs of engineering colleges in India. • We consider parallel non-homogeneous sub-processes in Data Envelopment Analysis (DEA) model. • We proposed the variable return to scale model for parallel non-homogeneous sub-processes in DEA. • We applied the above methodology to engineering colleges in India, with teaching and research being sub-processes. • We found that engineering colleges are more efficient in teaching than research. • With a ranking system like the NIRF, colleges have improved their efficiency. [ABSTRACT FROM AUTHOR]

Benchmarking, a critical process to compare relative performance of nursing homes, was implemented in an earlier study applying multicriteria measures of operational and quality of care efficiencies. In that study those measures were aggregated and optimized using Data Envelopment Analysis (Shimshak et al., 2009) [1]. The authors concluded that while providing useful analysis, the DEA results did not discriminate well among the nursing homes. In this paper we apply the Order Rated Effectiveness (ORE) model (Klimberg and Ratick, 2018; Klimberg and Ratick, 2020a; Klimberg and Ratick, 2020b) [2,3,4], to the Shimshak et al. data set to address that issue. The ORE model utilizes Order Weighted Averaging (OWA) to aggregate operational and quality performance measures, providing a range of relative performance measures for each nursing home. These aggregates are then used within a Data Envelopment Analysis (DEA) optimization framework. The use of OWA helps capture the implied preferences of multiple decision makers within the different nursing homes. We compare and evaluate the ORE results to those obtained in the original Shimshak et al. study. The ORE process provided aggregate comprehensive measures of relative performance that addressed both the multi-objective and multi-decision-maker character of this decision problem essential to making effective and actionable comparisons of nursing home performance. • Developed the Order Rated Effectiveness model to compare relative performance. • Order Rated Effectiveness model provides operational and quality performance measures. • The model integrates the multi-objective and multi-decision-maker nature of the problem. • Model results produced discriminating outcomes. • Model results provide areas on which to focus to improve performance. [ABSTRACT FROM AUTHOR]

This paper presents a network data envelopment analysis (NDEA) model to assess road transport sector by considering energy and non-energy inputs, shared input, and desirable and undesirable outputs. The sector is considered as a two-stage framework where in stage 1, energy-environmental efficiency is calculated and in stage 2, technical efficiency is estimated. There are two general approaches for evaluation of the network structures using NDEA models: bottom-up and top-down. In the bottom-up approach of this study, a goal programming NDEA (GP-NDEA) model is proposed to calculate the efficiency of the two stages at the same time and then overall efficiency is obtained using the results of the two stages. In the top-down approach, both non-radial SBM-NDEA and RAM-NDEA models are applied to calculate the overall efficiency and then the efficiencies of stage 1 and stage 2 are estimated using the results of the overall efficiency. Three different models GP-NDEA, SBM-NDEA, and RAM-NDEA lead to different results and to obtain the final efficiencies and ranks, principal component analysis (PCA) method is applied. The efficiency generated by different bottom-up and top-down approaches are considered as indicators in PCA, and the final scores for both stages are calculated using PCA. To illustrate the capability of the proposed approach, an actual dataset of road transport sector in provinces of Iran is gathered and the results of the different models are analyzed. • Introducing a two-stage network to measure the efficiency of road transport sector. • Managing undesirable output and shared resource in the proposed network. • Measuring the efficiency using top-down and bottom-up NDEA approaches. • Combining the results of different approaches using PCA. [ABSTRACT FROM AUTHOR]

In most production activities, desirable and undesirable outputs are produced together, and they are interdependent. As a result, reducing undesirable outputs can also decrease desirable ones. This study introduces concepts and models and applies them to a practical example for the first time, considering the dependency of production between desirable and undesirable outputs in an SBM network system. The aim is to assess the sustainability of supply chains. To achieve this goal, a new network data envelopment analysis (NDEA) model is developed based on the Slack Based Model (SBM). A case study is presented to evaluate the sustainability of wheat supply chains, which demonstrates various aspects of the proposed model, which considers both desirable and undesirable outputs produced together. • A new SBM network model is introduced to handle the jointly produced unwanted (bad) outputs and desirable outputs. • The possible changes of desirable and unwanted outputs are combined into the NDEA model. • Managerial strategies for altering the desirable and unwanted (bad) outputs are provided. [ABSTRACT FROM AUTHOR]

Faced with increasingly prominent challenges in developing a knowledge-based economy, many countries have attached great importance to research and development (R&D) activities, particularly in industrial enterprises. Considering the heterogeneity in the development background across countries, this study examines industrial R&D performance from 2016 to 2020 by comparing the productivity of the top R&D enterprises across world-leading economies and industries. The metafrontier approach and the Malmquist productivity index using data envelopment analysis and inferential statistics are employed in an empirical analysis of 1155 enterprises in the European Union (EU), the United States (US), Japan, and China operating in six different industries. The results reveal that R&D productivity has improved over time, particularly in the last year observed. Moreover, the US was the most productive, whereas China exhibited the worst productivity levels. Finally, despite Consumer Goods & Services presenting the best average R&D performance, the highest improvements are observed more recently, especially in Healthcare & Pharmaceuticals and ICT Goods & Services. The results suggest that in addition to technology gaps, the COVID-19 pandemic had important implications for the industrial performance of top R&D enterprises. The findings of this research can guide improvements in R&D efficiency and consequently facilitate evidence-based decision-making in the business sector and policymaking at the national level. • The study examines the industrial performance of the top R&D enterprises. • The study is based on a metafrontier approach and the Malmquist productivity index. • R&D productivity has improved over time, particularly in the last year observed. • The COVID-19 pandemic had important implications for industrial R&D performance. • The findings can guide improvements in R&D efficiency. [ABSTRACT FROM AUTHOR]

In the face of a rapidly ageing population, China's healthcare system is under mounting pressure to meet growing demand for more and better services. This paper aims to identify opportunities to improve the efficiency and quality of healthcare services in China. We first evaluate the healthcare performance of China's 31 provincial regions during the 2005–2019 period. The hospital efficiency is computed by using a window-based super-efficiency composite Data Envelopment Analysis model, while the healthcare quality is evaluated by using a window-based Technique for Order Preference by Similarity to an Ideal Solution method. Then, we investigate the impacts of socioeconomic factors, such as government subsidies, marketization, per capita Gross Domestic Product and demographic structure, on regional healthcare performance by use of regression analysis. The main findings include: 1) The nationwide healthcare performance has improved from 2005 to 2019 in part due to China's rapid economic growth, but more progress has been achieved in improving hospital efficiency than healthcare quality. 2) Government subsidies can improve both regional healthcare quality and hospital efficiency. While marketization can enhance hospital efficiency, private hospitals provide lower quality of care than public hospitals. 3) Regional hospital efficiency and healthcare quality are positively correlated, but a causal relationship cannot be established based on our empirical evidence. Based on our findings, we have the following recommendations for policymaking: 1) The government should increase subsidies to medical institutions in order to improve healthcare quality as well as hospital efficiency. 2) More subsidies should be allocated to regions having a high old-age dependency ratio. 3) More quality control and improvement efforts should be taken at private hospitals. • A comprehensive evaluation of China's provincial healthcare performance during 2005–2019. • Investigation of the socioeconomic determinants of regional healthcare performance. • Examination of the impacts of government subsidies and marketization on regional healthcare performance. • Analysis of the relationship between hospital efficiency and healthcare quality. [ABSTRACT FROM AUTHOR]

This study aimed to explore the possibility of measuring district health center (DHC) performance using effectiveness-based hierarchical data envelopment analysis (H-DEA). To reflect the overall performance in DHCs, four different scopes of services, namely, disease prevention and control, family health development, health infrastructure and clinical, and governance, were provided by DHCs. The findings show that H-DEA analysis is a possible approach to measure the efficiency of DHCs in Malaysia compared to conventional key performance indicator assessment. This finding hopes to provide guidance to policy makers on DHC service ranking in controlling and distributing the budget pertaining to public health activities at the national level. • Developing a performance indices of district health center (DHCs) in Malaysia. • A hierarchical data envelopment analysis model is applied. • Endogenous assignment of weight provides fair comparison across DHCs. • Health education and nutritional should be given priority and focus among DHCs. [ABSTRACT FROM AUTHOR]

We undertake a systematic comparison of existing models measuring and decomposing the economic efficiency of organizations. For this purpose we introduce the package BenchmarkingEconomicEfficiency.jl for the open-source Julia language including a set of functions to be used by scholars and professionals working in the fields of economics, management science, engineering, and operations research. Using mathematical programming methods known as Data Envelopment Analysis, the software develops code to decompose economic efficiency considering alternative definitions: profit, profitability, cost and revenue. Economic efficiency can be decomposed, multiplicative or additively, into a technical (productive) efficiency term and a residual term representing allocative (or price) efficiency. We include traditional decompositions like the radial efficiency measures associated with the input (cost) and output (revenue) approaches, as well as new ones corresponding to the Russell measures, the directional distance function, DDF (including novel extensions like the reverse DDF, modified DDF, or generalizations based on Hölder norms), the generalized distance function, and additive measures like the slack based measure, their weighted variants, etc. Moreover, regardless the underlying economic efficiency model, many of these technical inefficiency measures are available for calculation in a computer software for the first time. This article details the theoretical methods and the empirical implementation of the functions, comparing the obtained results using a common dataset on Taiwanese Banks. • We present an open-source Julia package for measuring economic efficiency. • Economic efficiency is decomposed into technical and allocative efficiencies. • Each economic efficiency model is decomposed with different technical measures. • We consider Farrell's measures, Russell, weighted additive, directional functions, etc. • We discuss the pros and cons of each decompositions based on desirable properties. [ABSTRACT FROM AUTHOR]

This study investigates the capacity utilization (CU) of Chinese manufacturing industries, using a CU indicator based on data envelopment analysis and directional distance functions (DDFs). The inputs are separated into variable inputs and a quasi-fixed input to measure the gap of DDFs, which indicated either under-utilization of inputs or overcapacity. Moreover, we define an indicator for CU change over time and introduced the corresponding decomposition. We note that, during the study time period (2007–2010), the CU of Chinese manufacturing industries improved, which implies that Chinese manufacturing industries expanded their production and got closer to their capacity during the examined period. The driving force of this improvement is technical changes. The higher average CU values of light manufacturing industries than that of the heavy industries and the extremely high CU values of two light industries reveal a severe overcapacity problem in the light industries. We also provided the methods and conduct analysis on determining optimal variable inputs and the type of the overcapacity on specific DMUs. The bootstrap regression procedures are employed to test the influence of environmental variables on CU values. Finally, we provide policy implications and suggestions for policymakers who oversee the development of Chinese manufacturing industries. • A CU indicator based on DEA and DDFs is defined to investigate Chinese manufacturing industries. • An indicator for CU change over time and its decomposition are also developed. • We find that the Chinese manufacturing industries exerted production potentials during the period. • The driving force of the above improvement comes from the technical changes. [ABSTRACT FROM AUTHOR]

The Swedish district courts have undergone a substantial restructuring process in which the main reform has been to merge. As a result, the number of district courts has declined from 95 in 2000 to only 48 in 2009. All main arguments that support merging concern enhancements of efficiency. However, it has not yet been explicitly examined whether the mergers have the potential to increase efficiency ex ante. Thus, the expectation concerning higher efficiency was built on a subjective view. This paper investigates whether the mergers can be rationalized from a production economic point of view. Data envelopment analysis (DEA) is used to compute a production frontier where the conducted mergers are incorporated to identify the potential ex ante gains. Furthermore, the overall potential is decomposed into learning, scale, and harmony to investigate the source of the potential gain, e.g., an effect of adjusting to best practice or a pure merging effect such as scale. The results show diverse potentials, i.e., a number of mergers did not have the potential to gain in efficiency while others could gain substantially. A conclusion based on the analysis is that the potential production economic effects should be investigated before merger decisions are made in the future. This is also likely to be true beyond the Swedish district courts. • We investigate whether the actual mergers of Swedish district courts can be rationalized based on ex ante information. • The potential ex ante gains of merging are decomposed into learning, harmony and scale. • A substantial number of mergers do not indicate any efficiency gains, while others show large potentials. • This is also likely to be true beyond the Swedish district courts. [ABSTRACT FROM AUTHOR]

Abstract Regarding the importance of budgeting in organizations, this research proposes an empirical approach to budget allocation problems. The methodological instrument utilized is data envelopment analysis (DEA) which is a nonparametric mathematical programming technique. In the DEA methodology a standard DEA model should be independently solved to evaluate each decision making unit (DMU). Consequently, it is hard to find the magnitude of budget for each DMU by applying a budget allocation model based on standard DEA models because identifying the DMU under evaluation is problematic. Also, to overcome problems of evaluation using standard DEA models, common set of weights (CSW) DEA models were suggested. These models can be developed for use in budget allocation DEA models that lead to finding a single magnitude of budget for each DMU. Moreover, the opinion of the decision maker can be incorporated into the model using budgetary constraints. As a result, a restricted linear budget allocation CSW DEA model is proposed in which the central authority would like to plan for improving the total efficiency scores of all DMUs. In essence, the proposed model is used to reallocate the available budget and, thus, the results obtained will be a suggestion for budget allocation in subsequent periods. Finally, the proposed model is applied to budget allocation in the Iranian gas industry in which the available budget is reallocated to increase the total efficiency scores of Iranian gas distribution branches. Highlights • This research proposes an empirical approach to budget allocation problems. • The proposed model for use in budget allocation is a CSW DEA model. • The proposed model is based on MCDM; thus, it has a robust background. • Indeed, the proposed model is used to reallocate the available budget. • The proposed model is applied to the optimizing budget allocation in Iranian gas industry. [ABSTRACT FROM AUTHOR]

Abstract Considering the interrelationships between periods and the influences of non-operational factors, a new framework based generalized three-stage DEA model, grey relational analysis theory and disparity disassembly model is proposed in this paper. Then, we measure the technical efficiency, scale efficiency, and pure technical efficiency of innovation in China's semiconductor industry between 2009 and 2014. In addition, we conducted projection analysis of the inputs to innovation and the disparities analysis in innovation efficiency across the industrial chain and within each segment. The results of our analyses reveal four key findings. The overall innovation efficiency of China's semiconductor industry is increasing; however, each segment of the industrial chain had different trends and different levels of innovation efficiency. All segments show a consistently upward trend except for package testing, which dipped in 2012 due to the time lag between the inputs and outputs associated with major technological advancements. The most efficient innovation is occurring in design and package testing, followed by manufacturing, materials, and equipment, in that order. Low levels of innovation efficiency were found to be the most significant factor restricting further improvement in the design, manufacturing, and equipment segments of the industrial chain. But the opposite is true for the package testing segment, where pure technical efficiency is the main factor. A range of redundancies in input were found across the industrial chain, mostly in manufacturing and equipment. These two segments are capital-intensive and characterized by a high level of technical complexity coupled with a long research cycle. The disparities in innovation efficiency in and between the segments decreased over the period. However, interestingly, the main disparities were found among the enterprises within each segment, which we attribute to the Chinese government's concerted efforts to support particular companies. Package testing and manufacturing had the highest levels of disparity due to relatively high agglomeration of these two segments. The materials segment had the lowest disparity, with equipment and design falling somewhere in-between. Highlights • We proposed a generalized three-stage DEA method to measure the innovation efficiency of China's semiconductor industry. • Projection analysis of innovation inputs and disparities analysis in innovation efficiency are further conducted. • The innovation efficiency of industry is increasing and each segment of the industrial chain has different trends. • A range of redundancies in input were found across the industrial chain, mostly in manufacturing and equipment. • The main disparities in innovation efficiency were found among the enterprises within each segment. [ABSTRACT FROM AUTHOR]

Abstract This paper employs a Russell multi-activity network DEA model and divides the overall innovation process into the upstream Research and Development (R&D) process and the downstream commercialization process to appraise the innovation performance of China's high-tech industries from 2009 to 2013. This model can deal with the problems of intermediates, shared inputs and slack-based measure in a unified framework, and the result can provide policy makers with process-specific information on how to improve the innovation performance of China's high-tech industries. The main findings are presented as follows. First, the overall efficiency of China's high-tech industries still remains at a low level, which has its roots mainly in commercialization inefficiencies other than R&D inefficiencies. Second, for most provinces, their R&D efficiencies do not match up with their commercialization efficiencies. Finally, the innovative activities of China's high-tech industries should be driven by the market demand -oriented for the improvement of innovation efficiency. Highlights • It provide multi-activity network data envelopment analysis to appraise the China's high-tech industry perform. • It provides a two-stage production structure of the operational activities to interpret the innovation and R&D activities measurement. • The result shows that the overall efficiency of China's high-tech industries still remains at a low level. [ABSTRACT FROM AUTHOR]

Abstract This article examines public procurements in the regional bus transportation sector. The data were collected exclusively for this research from regional offices in the Czech Republic. This study does not propose to estimate the cost function for bus operators, as do most efficiency studies. Rather, it' focuses on identifying the factors that influence the final price offered by participants in the tender. Knowing these factors will help to properly set the tender conditions, which can further help to improve how well the bus operator functions, and to reduce public subsidies. The study also aims to estimate the technical efficiency rate by using Data Envelopment Analysis (DEA) and find the implications of efficiency scoring for tender conditions. This statistic also enables contract owners to compare the cost differences between individual bus service providers and examine companies that operate at the efficiency frontier. Highlights • Using DEA in order to find the implications of efficiency scoring for tender conditions in bus transportation industry. • Majority of DMUs operate under decreasing returns to scale. • Larger service size is a key factor in setting optimal public procurements' conditions. • No evidence that the number of bidders has a significant effect on the final price. • Evidence that public procurements in general result in lower prices. [ABSTRACT FROM AUTHOR]

Abstract In Brazil, which has one of the biggest energy systems in the world, the National Electricity Regulatory Agency (Agência Nacional de Energia Elétrica – ANEEL) regulates the generation, transmission, distribution and commercialization of electricity. As a key step in running the incentive regulation regime, the regulator applies a DEA-based Malmquist index for estimating the X-factor, which reflects productivity gains that can be expected across all companies involved in the industry. This paper investigates the way the X-factor was estimated in 2015 by ANEEL in the transmission sector concerning the review period 2009–2014. The regulator applies a modified version of the Malmquist index developed by Ray and Desli [1] and also adapts the bootstrapping method of Simar and Wilson [2] to generate confidence intervals for an estimate of the X-factor. Analyzing ANEEL's approach, we have discovered a number of serious analytical and computational shortcomings, which we demonstrate in the paper. On this basis, we address the effect of the estimated X-factor on social welfare, having the final customers to pay a higher price for electricity than they were supposed to. Highlights • We address the regulation for electricity transmission system operators in Brazil. • A key issue is the X-factor, which should reflect the industry's productivity gains. • We investigate the way the X-factor was estimated in 2015 based on 2009–2014 data. • A number of serious analytical and computational shortcomings are revealed. • On this basis, we discuss the effect of the estimated X-factor on the market. [ABSTRACT FROM AUTHOR]

Abstract In 2014 the Brazilian Electricity Regulator (ANEEL) evaluated the efficiency of power distribution utilities using Data Envelopment Analysis (DEA). Estimated efficiencies range from 22.46% to 100%. Although environmental information is available in the data set, corrected efficiencies were not investigated. Different second stage models can be applied to adjust for environmental heterogeneity. Although statistical correlation among efficiencies and environmental variables can be easily estimated, corrected efficiencies are subject to the underlying structure of the second stage model. Therefore, different second stage models may achieve different corrected efficiencies. We provide a detailed statistical analysis of the Tobit model and compound error models for second stage analysis. Limitations are described and the corrected efficiencies using these models are evaluated. Potentially, Brazilian power distribution utilities may achieve substantial changes in estimated efficiencies if second stage analysis is used. Highlights • Statistical properties of DEA second stage analysis are presented. • Second stage models with compound error are evaluated. • A second stage analysis applied to Brazilian benchmarking model is presented. • Results show significant changes in efficient operational costs using second stage analysis. [ABSTRACT FROM AUTHOR]

Abstract In Italy, public state libraries are multi-product organizations preserving ancient books of great historical relevance for future generations, and providing divisible services to the public. Hence, they may undertake different activities for conservation and use, which together constitute a network. This paper shows the importance of considering multi-process interactions in evaluating the overall performance of public state libraries and focuses on library operations and their sub-processes. It uses a network two-stage Data Envelopment Analysis (DEA) approach to examine the relationship between the libraries' basic inputs, intermediate outputs and final outputs. The main result is that Italian public state libraries generally perform better in the first stage of conservation, but score poorly in the second stage of use. Some policy advice to improve the decision-making process follow Highlights • Public state libraries undertake different activities, which together create a network. • Comparison of network two-stage and standard DEA to assess the performance. • Standard DEA tend to overstate efficiency of libraries for conservation and use. • Efficiency in each stage reveals the presence of sub-technologies absent in standard DEA. • Most of the inefficiencies are produced in the second stage of the production process. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine whether Indian banks were able to weather the COVID-19 storm. We estimate banks' deposits-generating and operating efficiencies using a two-stage directional distance function-based network data envelopment analysis (DDF-NDEA) approach and seek to capture the immediate impact of COVID-19 on these efficiency measures by comparing their magnitudes in the pre-pandemic (2014/15–2019/20), just 1-year prior to the pandemic (2019/20), and during the pandemic year (2020/21) periods. The study looks at whether the impact of the COVID-19 pandemic was uniform across ownership types and size classes. The empirical findings suggest that the Indian banking system was resilient and withstood the immediate impact of the COVID-19 pandemic. During the study period, however, the large and medium-sized banks experienced some efficiency losses. By and large, regardless of bank group, banks have shown resilience to the shock of the global health pandemic and improvements in efficiency. • We investigate the initial effect of COVID-19 on the deposits-generating and operating efficiencies of Indian banks. • We use a 2-stage directional distance function-based network DEA model with quasi-fixed inputs & undesirable inputs/outputs. • Findings show that the Indian banking system was resilient and withstood the immediate impact of the COVID-19 pandemic. • Large and medium-sized banks experienced some efficiency losses in their operating performance. [ABSTRACT FROM AUTHOR]

The development of higher education institutions (HEIs) is related to the future overall development of higher education in China. Studying the efficiency of provincial HEIs in China has great significance in deepening the reform of higher education evaluation, promoting coordinated regional development, and building a country powerful in its higher education. Based on the Super-SBM method, this paper creatively integrates meta-frontier theory and an index of international exchange and cooperation into an evaluation system for higher education efficiency. Taking 2011–2020 as the sample period, this study conducts a dynamic assessment of the higher education efficiency in 30 mainland Chinese provinces. It explores the differences in production technology in the eastern, central, and western regions, analyzing the environmental factors that affect the efficiency of higher education. The results show that during the sample period, the overall efficiency of higher education in China improved steadily. Efficiency Change is the main driving force. The three regions have significantly different technology gap ratio, and the structure of resource allocation significantly impacts efficiency. Therefore, this study's main recommendations are that the Chinese government should not only deepen the coordinated development of higher education between regions but also pay more attention to solving the problem of unbalanced development between China's geographical regions. At the same time, educational authorities should expand the number of ordinary colleges and universities in the central and western regions, develop non-financial funding sources, and improve the level of refined management of HEIs. • This study measures the efficiency and evolution of China's HESs in the second decade of the 21st century. • It considers the effects of regional heterogeneity on the efficiencies of HESs. • It incorporates higher vocational colleges into HES efficiency evaluation. • The result shows that the efficiency of Chinese HES show obvious geographical characteristics. [ABSTRACT FROM AUTHOR]

To obtain high-quality human resources and to gain advantages in the context of global competition, countries are constantly promoting the development of higher education. Enormous human and financial resources were invested in this "battle". However, the "inputs" consumed by universities are largely ignored. A major issue, evaluating how well a country's universities make use of input resources in their output path, is of economic and social significance. The focus of this study is to embed the concept of capacity utilization in education and to assess the university's performance. We construct an attainable output-oriented capacity utilization measurement model by taking into account the heterogeneity of the region. According to the usage of the input, we divide the decision making units into four areas and identify the causes of inefficiency. Thus, the methodology proposed in this study provides policymakers an applicable tool on differences in educational inputs across regions. To illustrate the method, we conduct an empirical analysis using data from 54 universities directly under the Ministry of Education in China. • By embedding the concept of CU in the education sector, we propose a conceptual theoretical framework and assessing the utilization of university inputs. • We construct an attainable output-oriented CU measurement model by taking into account the attainability of variable inputs. • By dividing decision making units into four types according to the causes of inefficiencies, this paper proposes an CU analysis paradigm. [ABSTRACT FROM AUTHOR]

In recent years more and more numerous are the rankings published in newspapers or technical reports available, covering many aspects of higher education, but in many cases with very conflicting results between them, due to the fact that universities' performances depend on the set of variables considered and on the methods of analysis employed. This study measures the efficiency of Italian higher education using both parametric and non-parametric techniques and uses the results to provide guidance to university managers and policymakers regarding the most appropriate method for their needs. The findings reveal that, on average and among the macro-areas of the country, the level of efficiency does not change significantly among estimation approaches, which produce different rankings, instead. This may have important implications as rankings have a strong impact on academic decision-making and behaviour, on the structure of the institutions and also on students and graduates recruiters. [ABSTRACT FROM AUTHOR]

In recent years there has been an exponential growth in the number of publications related to theory and applications of Data Envelopment Analysis (DEA). Charnes, Cooper, and Rhodes (1978) introduced DEA as a tool for measuring efficiency and productivity of decision making units. DEA has immediately been recognized as a modern tool for performance measurement. Since then, a large and considerable amount of articles has been appeared, including significant breakthroughs in theory and a great portion of works on DEA applications, both public and private sectors, to assess the efficiency and productivity of their activities. Although there have been several bibliographic collections reported, a comprehensive analysis and listing of DEA-related articles covering its first four decades of history is still missing. This paper, thus, aims to report an extensive listing of DEA-related articles including theory and methodology developments and "real" applications in diversified scenarios from 1978 to end of 2016. Some summary statistics of the publications' growth, the most utilized academic journals, authorship analysis, as well as keywords analysis are also provided. [ABSTRACT FROM AUTHOR]

Research on productive efficiency at the firm level has developed as an important and active strand of research the last decades, both within operations research, management science and economics. Two apparently different definitions of efficiency are examined, but it is shown that when both estimation methods are based on solving linear programming problems the definitions of efficiency are identical. The purpose of the paper is to give the basic ideas of efficiency analyses using DEA as a tool for researchers not so familiar with efficiency analysis and DEA. The concept of shadow prices is given special attention. [ABSTRACT FROM AUTHOR]

This study focuses on the allocation of R&D resources in R&D active firms. We utilize the input oriented constant (CRS) and variable (VRS) returns to scale data efficiency analysis models to evaluate the efficiency of firms. Scale efficiency and the respective types of returns to scale have been examined by using DEA models with ratio inputs and outputs. We pay attention to the global frontier and the firm's own sector and size frontiers. We highlight the sources of inefficiency and suggestions are proposed to improve efficiencies of R&D resources allocation. The analysis is based on a representative set of (quasi-) permanent R&D active firms in Belgium. We consider R&D related inputs in the year 2009 and include firm performance in terms of turnover and net added value per employee in a four year time span. The paper highlights that on average, R&D active firms suffer from both technical inefficiency and scale size problems since the average of the CRS and the VRS efficiency are low, and also the average of scale efficiency is modest. According to firm size, small-sized firms suffer from scale and technical inefficiency. Medium-sized firms endure scale inefficiency rather than technical inefficiency. Large firms present a higher average scale efficiency and technical efficiency. According to sector of activity, firms in specialized supplier industries tend to outperform other firms in terms of average scale efficiency and average technical efficiency. Firms in science based industries are found to underperform on average in terms of VRS and scale efficiency. [ABSTRACT FROM AUTHOR]

In data envelopment analysis (DEA) and with a variable returns-to-scale (VRS) technology, we implement Baumol et al.’s [1] concept of “cost minimizing industry structure”, which features reallocation of outputs and a variable number of firms. The characterization of this type of optimal allocation adds to the literature on structural efficiency, which so far has not dealt with it in such a general framework. At the theoretical level, we both determine a decomposition of the industry measure, which establishes the relationship between group and individual measures, and provide its rigorous economic interpretation based on the ray average cost. Moreover, our framework allows to highlight the relationship between the industry measure and different returns-to-scale characterizations of the technology. At the applicative level we devise an algorithm to solve the related non-linear programming problem, thus providing the decision maker with a method to compute the optimal industry structure and the corresponding efficiency components. Empirical illustration is given with reference to the Italian local-public-transit sector employing a multiple input and output technology. [ABSTRACT FROM AUTHOR]

The paper presents a Data Envelopment Analysis aimed at studying the efficiency of Tuscan municipalities' public expenditure. Five strategic functions of Tuscan municipalities are first considered carrying out a non-aggregate analysis; then the overall expenditure composition of each municipality and the global spending efficiency are analysed by a proposed composite indicator. The main determinants affecting the municipalities' efficiency were further investigated. In particular, the obtained results may be consistently included in the long-standing debate on the municipal size, proving that the bigger the municipality, the greater its level of public expenditure efficiency. [ABSTRACT FROM AUTHOR]